RESUMEN

The chemistry of sulfur cycle contributes significantly to the atmospheric nucleation process, which is the first step of new particle formation (NPF). In the present study, cycloaddition reaction mechanism of sulfur trioxide (SO3) to hydrogen sulfide (H2S) which is a typical air pollutant and toxic gas detrimental to the environment were comprehensively investigate through theoretical calculations and Atmospheric Cluster Dynamic Code simulations. Gas-phase stability and nucleation potential of the product thiosulfuric acid (H2S2O3, TSA) were further analyzed to evaluate its atmospheric impact. Without any catalysts, the H2S + SO3 reaction is infeasible with a barrier of 24.2 kcal/mol. Atmospheric nucleation precursors formic acid (FA), sulfuric acid (SA), and water (H2O) could effectively lower the reaction barriers as catalysts, even to a barrierless reaction with the efficiency of cis-SA > trans-FA > trans-SA > H2O. Subsequently, the gas-phase stability of TSA was investigated. A hydrolysis reaction barrier of up to 61.4 kcal/mol alone with an endothermic isomerization reaction barrier of 5.1 kcal/mol under the catalytic effect of SA demonstrates the sufficient stability of TSA. Furthermore, topological and kinetic analysis were conducted to determine the nucleation potential of TSA. Atmospheric clusters formed by TSA and atmospheric nucleation precursors (SA, ammonia NH3, and dimethylamine DMA) were thermodynamically stable. Moreover, the gradually decreasing evaporation coefficients for TSA-base clusters, particularly for TSA-DMA, suggests that TSA may participate in NPF where the concentration of base molecules are relatively higher. The present new reaction mechanism may contributes to a better understanding of atmospheric sulfur cycle and NPF.

Asunto(s)

RESUMEN

Emission characteristics of biogenic volatile organic compounds (BVOCs) from dominant tree species in the subtropical pristine forests of China are extremely limited. Here we conducted in situ field measurements of BVOCs emissions from representative mature evergreen trees by using dynamic branch enclosures at four altitude gradients (600-1690 m a.s.l.) in the Nanling Mountains of southern China. Composition characteristics as well as seasonal and altitudinal variations were analyzed. Standardized emission rates and canopy-scale emission factors were then calculated. Results showed that BVOCs emission intensities in the wet season were generally higher than those in the dry season. Monoterpenes were the dominant BVOCs emitted from most broad-leaved trees, accounting for over 70% of the total. Schima superba, Yushania basihirsuta and Altingia chinensis had relatively high emission intensities and secondary pollutant formation potentials. The localized emission factors of isoprene were comparable to the defaults in the Model of Emissions of Gases and Aerosols from Nature (MEGAN), while emission factors of monoterpenes and sesquiterpenes were 2 to 58 times of those in the model. Our results can be used to update the current BVOCs emission inventory in MEGAN, thereby reducing the uncertainties of BVOCs emission estimations in forested regions of southern China.

Asunto(s)

RESUMEN

Designing persistent dual-band afterglow materials with thermally activated delayed fluorescence (TADF) and room temperature phosphorescence (RTP) contributed to solving the problems of homogenization and singularity in long afterglow materials. Here, six aryl acetonitrile (CBM) and aryl dicyanoaniline (AMBT) derivatives, used as host and guest materials respectively, were successfully designed and synthesized based on the isomerization effect. Among of them, 0.1 % m-CBM/p-AMBT showed the longest dual-band TADF (540 ms) and RTP lifetimes (721 ms), as well as persistent afterglow over 8 s, whose fluorescence (ΦFL), TADF (ΦT) and RTP (ΦP) quantum yields were 0.11, 0.06 and 0.22 in sequence. More interestingly, some doping systems constructed by CBM and AMBT series compounds showed persistent triple-band emissions composed of TADF, unimolecular and aggregated AMBT series compounds. What's more, ΦFL, ΦT and ΦP of 1 % o-AMBT@PMMA film were up to 0.17, 0.17, 0.23 in turn, with TADF, RTP and afterglow lifetimes of 606 ms, 727 ms, and 10 s respectively. TADF and RTP emission of CBM/AMBT series doping systems was attributed to host sensitized guest emission. Besides, the comparison displayed AMBT series compounds had bigger intensity ratios between TADF and RTP emission in PMMA films compared to in CBM series compounds. Finally, a series of data encryption were successfully constructed based on different afterglow lifetimes of the doping systems, and a dynamic anti-counterfeiting pattern was prepared by using different temperature responses of TADF and RTP emissions.

RESUMEN

High-pressure processing (HPP) of donor human milk (DM) minimally impacts the concentration and bioactivity of some important bioactive proteins including lactoferrin, and bile salt-stimulated lipase (BSSL) compared to Holder pasteurization (HoP), yet the impact of HPP and subsequent digestion on the full array of proteins detectable by proteomics remains unclear. We investigated how HPP impacts undigested proteins in DM post-processing and across digestion by proteomic analysis. Each pool of milk (n = 3) remained raw, or was treated by HPP (500 MPa, 10 min) or HoP (62.5 °C, 30 min), and underwent dynamic in vitro digestion simulating the preterm infant. In the meal, major proteins were minimally changed post-processing. HPP-treated milk proteins better resisted proximal digestion (except for immunoglobulins, jejunum 180 min) and the extent of undigested proteins after gastric digestion of major proteins in HPP-treated milk was more similar to raw (e.g., BSSL, lactoferrin, macrophage-receptor-1, CD14, complement-c3/c4, xanthine dehydrogenase) than HoP.

Asunto(s)

RESUMEN

The abundance of uranium (U(VI)) reserves in seawater makes it crucial to develop economically efficient methods for uranium extraction from seawater. In this work, an enhanced polyamidoxime porous membrane (PAOM) was fabricated by pre-in situ amidoxime modification combined with nonsolvent-induced phase separation (NIPS). The strategy of in situ modification of the polyacrylonitrile (PAN) solution served to enhance the homogeneity of the reaction and avoid the destruction of the membrane matrix and pore structure. Compared with the control sample (AOPM), PAOM possessed better mechanical strength and hydrophilicity. The introduction of polyvinylpyrrolidone (PVP) formed a porous structure in PAOM, improving spatial accessibility and facilitating the diffusion transport and capture of UO22+ inside the membrane. The more uniform and abundant distribution of amidoxime groups in PAOM gave it ultrahigh adsorption capacity and selectivity. The equilibrium adsorption capacity and Kd value of PAOM were 1.72 and 5.51 times higher than those of AOPM. Meanwhile, PAOM also demonstrated good recyclability, with only a 6.15% decrease in adsorption capacity after seven cycles. Additionally, PAOM exhibited excellent dynamic adsorption performance, and after 14 days of continuous filtration and adsorption, PAOM could extract 2.03 mg·g-1 U(VI) from natural seawater.

RESUMEN

Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function.

Asunto(s)

RESUMEN

BACKGROUND: Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. MAIN BODY: We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. CONCLUSIONS: Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Asunto(s)

RESUMEN

Oxide derived catalyst displays outstanding catalytic activity and selectivity in electrochemical carbon dioxide reduction reaction (CO2RR), in which, it is found that residue oxygen atoms play a pivotal role in regulating the catalyst's electronic structure and thus the CO2RR process. Unfortunately, the intrinsic thermodynamic instability of oxygen atoms in oxide derived catalyst under cathodic CO2RR potentials makes it unstable during continuous electrolysis, greatly hindering its practical industrial applications. In this work, we develop a pulsed-bias technique that is able to dynamically stabilize the residue oxygen atoms in oxide derived catalyst during electrochemical CO2RR. As a result, the oxide derived catalyst under pulsed bias exhibits super catalytic stability in catalyzing electrochemical CO2RR, while keeping excellent catalytic activity and selectivity.

RESUMEN

The study aimed to investigating the mechanisms of relieved intestinal barrier damage by dynamic high-pressure microfluidization assisted with galactooligosaccharide- glycated whey protein isolate. The modifications changed the multi-structure, and the modified whey protein isolate could promote the proliferation of IEC-6 cells and contributed to the restoration of LPS-induced occludin damage in IEC-6 cells. Also, it could repair cyclophosphamide-induced ileal villus rupture and crypt destruction in BALB/c mice, significantly altered the abundance of dominant bacteria, which were associated with propionic acid, butyric acid, isovaleric acid, and valeric acid. Ileum transcriptomics revealed that the modified whey protein isolate significantly regulate of the levels of Cstad, Cyp11a1, and Hs6st2 genes, relating to the increase of propionic acid, isovaleric acid, and valeric acid. In conclusion, galactooligosaccharide- modified whey protein isolate could regulate the level of Cstad, Cyp11a1 and Hs6st2 genes by altering the gut microbial structure and the level of SCFAs, thereby repairing the intestinal barrier.

RESUMEN

The low melt strength and brittleness of polylactic acid (PLA) significantly limit its application in high-performance and functional films. In this study, we successfully prepared ternary thermoplastic vulcanizates (TPVs) comprising of PLA, poly(caprolactone) (PCL) and natural rubber (NR), which exhibit co-continuous structure. During the peroxide-induced dynamic vulcanization process, PLA chains were effectively grafted and anchored to the cross-linked rubber network, thereby enhancing the melt strength of the material. This improvement allowed the ternary TPVs to be continuously and stably blown into films. The films exhibited excellent mechanical properties due to the synergistic effect of ductile PCL and elastic NR. Both the elongation at break and tear strength improved greatly. Interestingly, during the film blowing process, the highly stretched continuous NR crosslinked network stored large amounts of elastic energy, endowing the film with excellent heat shrinkage performance. With 15 phr NR, the heat shrinkage exceeded 35 %, which is sufficient for practical needs. This biodegradable film with heat-shrinkage properties can be processed on a large scale, offering a potential strategy to the urgent issue of plastic film pollution.

RESUMEN

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton-Jacobi-Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed.

RESUMEN

Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data.

RESUMEN

Precise finite element modeling is critically important for the construction and maintenance of long-span suspension bridges. During the process of modeling, shape-finding and model calibration directly impact the accuracy and reliability. Scholars have provided numerous alternative proposals for the shape-finding of main cables in suspension bridges from both theoretical and finite element analysis perspectives. However, it is difficult to apply these solutions to suspension bridges with special components. Seeking a viable solution for such suspension bridges holds practical significance. The Nanjing Qixiashan Yangtze River Bridge is the first three-span suspension bridge in China. To maintain the configuration of the main cable, the suspension bridge is equipped with specialized suspenders near the anchors, referred to as displacement-limiting suspenders. It is the first suspension bridge in China to use displacement-limiting suspenders and their anchorage system. Taking the suspension bridge as a research background, this paper introduces a refined finite element modeling approach considering the effect of geometric nonlinearity. Firstly, based on the loop adjustment and temperature correction, the shape-finding and force assessment of the main cables are carried out. On this basis, a nonlinear finite element model of the bridge was established and calibrated, taking into account factors such as pylon settlement and cable saddle precession. Finally, the static and dynamic characteristics of the suspension bridge were thoroughly investigated. This study aims to provide a reference for the design, construction and operation of the three-span continuous suspension bridge.

RESUMEN

In this study, nanoporous gold (NPG) was deposited on a screen-printed carbon electrode (SPCE) by the dynamic hydrogen bubble template (DHBT) method to prepare an electrochemical sensor for the simultaneous determination of Pb2+ and Cu2+ by square wave anodic stripping voltammetry (SWASV). The electrodeposition potential and electrodeposition time for NPG/SPCE preparation were investigated thoroughly. Scanning electron microscopy (SEM) and energy-dispersive X-ray diffraction (EDX) analysis confirmed successful fabrication of the NPG-modified electrode. Electrochemical characterization exhibits its superior electron transfer ability compared with bare and nanogold-modified electrodes. After a comprehensive optimization, Pb2+ and Cu2+ were simultaneously determined with linear range of 1-100 µg/L for Pb2+ and 10-100 µg/L for Cu2+, respectively. The limits of detection were determined to be 0.4 µg/L and 5.4 µg/L for Pb2+ and Cu2+, respectively. This method offers a broad linear detection range, a low detection limit, and good reliability for heavy metal determination in drinking water. These results suggest that NPG/SPCE holds great promise in environmental and food applications.

RESUMEN

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety-distance-error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety-distance-error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms.

RESUMEN

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine.

RESUMEN

Fusing data from many sources helps to achieve improved analysis and results. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle's surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. Furthermore, we also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereo vision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

RESUMEN

Introduction: Advanced traffic monitoring systems face significant challenges in vehicle detection and classification. Conventional methods often require substantial computational resources and struggle to adapt to diverse data collection methods. Methods: This research introduces an innovative technique for classifying and recognizing vehicles in aerial image sequences. The proposed model encompasses several phases, starting with image enhancement through noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Following this, contour-based segmentation and Fuzzy C-means segmentation (FCM) are applied to identify foreground objects. Vehicle detection and identification are performed using EfficientDet. For feature extraction, Accelerated KAZE (AKAZE), Oriented FAST and Rotated BRIEF (ORB), and Scale Invariant Feature Transform (SIFT) are utilized. Object classification is achieved through a Convolutional Neural Network (CNN) and ResNet Residual Network. Results: The proposed method demonstrates improved performance over previous approaches. Experiments on datasets including Vehicle Aerial Imagery from a Drone (VAID) and Unmanned Aerial Vehicle Intruder Dataset (UAVID) reveal that the model achieves an accuracy of 96.6% on UAVID and 97% on VAID. Discussion: The results indicate that the proposed model significantly enhances vehicle detection and classification in aerial images, surpassing existing methods and offering notable improvements for traffic monitoring systems.

RESUMEN

INTRODUCTION:  The three-dimensional (3D) dynamic navigation system (DNS; Navident, ClaroNav Technology, Toronto, ON) is a revolutionary technique in endodontics that offers superior precision and efficiency compared to existing techniques for post space preparation. AIM: The aim is to evaluate and contrast the efficacy and efficiency of the DNS with conventional post space preparation techniques. This assessment considers several parameters, notably canal deviation (global coronal and apical deviation and angular deflection), duration of the procedure, and total volumetric loss of instrumented root canal and volumetric loss of instrumented root canal above 4 mm from the apex. MATERIALS AND METHODS: Freshly extracted maxillary central incisors were chosen for this study. A total sample size of 60 (n) was included. The extracted teeth samples were divided into two groups: 3D DNS (group I; n = 30) and conventional techniques (group II; n = 30). The samples were taken, and 50% of the crown structure was reduced for post space preparation to ensure standardization between the two groups. The samples were root canal-treated and mounted in a 3D-printed maxillary cast. Preoperative micro-computed tomography (micro-CT) and cone-beam computed tomography (CBCT) were taken for both groups. For group I, post space preparation was conducted with the DNS, which provided comprehensive guidance. The procedure was stopped when post space preparation was 4 mm short of the apex, as indicated in the system display. For group II, post space preparation was done without the guidance of DNS. Time taken for the procedure was assessed using a timer; canal deviation was evaluated using CBCT analysis, and volumetric loss was estimated using micro-CT analysis. RESULTS: The dynamic navigation group achieves significantly more precise outcomes in post space preparation than the conventional technique. The DNS group has significantly lesser global coronal and apical deviation and angular deflection compared to the conventional group (p < 0.05). The DNS group has reduced the volumetric loss of instrumented root canals compared to the conventional group (p < 0.05). Furthermore, the DNS group requires significantly less time than the conventional method, with a mean difference of about 10.567 minutes (p < 0.05). CONCLUSION: Implementing dynamic navigation improves precision in post space preparation, with a notable reduction in canal deviation and volumetric loss and a decrease in procedure time compared to the conventional method.

RESUMEN

Background: Targeted therapy with neoadjuvant chemotherapy for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer has increased the rates of pathological complete response (pCR) and breast preservation surgery and improved the overall disease-free survival rate. This study aimed to determine whether tumor enhancement and shrinkage patterns in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the efficacy of targeted therapy in patients with HER2-positive breast cancer and differentiate pCR from non-pCR. Methods: The data of 64 patients with HER2-positive breast cancer who received targeted therapy prior to surgery were retrospectively collected. All patients had complete postoperative pathological data. The pretreatment evaluation of the tumor enhancement pattern and the shrinkage pattern after two treatment cycles were assessed. The difference in the enhancement and shrinkage patterns between the pCR and non-pCR groups was evaluated via the χ2 test. Logistic regression analysis was used to assess the value of enhancement and shrinkage patterns for predicting pCR in patients with HER2-positive breast cancer. Results: There were statistically significant differences in tumor size, estrogen receptor (ER) status, lymph node metastasis, enhancement pattern, and shrinkage pattern between the pCR and non-pCR cases. Patients with a tumor size ≤20 mm were likely to achieve pCR. ER status, lymph node metastasis, and enhancement and shrinkage patterns each had good precision for predicting pCR, and the combination of enhancement and shrinkage patterns had the highest prediction accuracy. Multivariate logistic regression analysis indicated that only enhancement pattern had a significant predictive value. Conclusions: Among patients with HER2-positive breast cancer, those with tumor size ≤20 mm, ER-negative status, no lymph node metastases, and mass enhancement and concentric shrinkage patterns are more likely to achieve pCR. Mass enhancement combined with concentric shrinkage had the highest accuracy in predicting pCR, indicating that preoperative imaging may be useful for guiding clinical decisions regarding targeted treatments.